Dependence Analysis with Normally Distributed Aggregate Claims in Stop Loss Insurance

Date

2017-12-08

Author

Mert, Özenç Murat
Kestel, Sevtap Ayşe

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The reinsurance contracts in the insurance market have been playing an important role in the last couple of decades. One of the most important reinsurance contracts is the stop-loss reinsurance. It has an interesting property from the insurer point of view such that it is optimal if the criterion of minimizing the variance of the cost of the insurer is used. The word ‘optimality’ takes many researcher’s attention so that optimal reinsurance contracts under different assumptions have been investigated for decades For instance, some researchers used utility functions to find the optimal contract while the others use aggregate claims with many different distributions such as gamma and translated gamma distributions. This study aims to examine the stop-loss contracts with priority and maximum under the assumption the aggreate claims with normal distribution. The dependence between the cost of the insurer and the cost of the reinsurer is taken into account by implementing traditional dependence measures. Additional to these, the impact of tail dependence captured by copula approach is investigated. The deterministic retention is found when the correlation between the cost of the insurer and the cost of the reinsurer is maximum. Moreover, if the contract includes a maximum, the converging of the correlation of the parties is examined according to the distance between the maximum and the priority.

URI

https://hdl.handle.net/11511/79422

Conference Name

10th Internatioanal Statistics Congress (ISC2017) Ankara, (6 - 08 Aralık 2017)

Collections

Graduate School of Applied Mathematics, Conference / Seminar

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Citation Formats

Ö. M. Mert and S. A. Kestel, “Dependence Analysis with Normally Distributed Aggregate Claims in Stop Loss Insurance,” presented at the 10th Internatioanal Statistics Congress (ISC2017) Ankara, (6 - 08 Aralık 2017), Ankara, Türkiye, 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/79422.